Same Exposure but Two Radically Different Responses to Antibiotics: Resilience of the Salivary Microbiome versus Long-Term Microbial Shifts in Feces

Abstract

Due to the spread of resistance, antibiotic exposure receives increasing attention. Ecological consequences for the different niches of individual microbiomes are, however, largely ignored. Here, we report the effects of widely used antibiotics (clindamycin, ciprofloxacin, amoxicillin, and minocycline) with different modes of action on the ecology of both the gut and the oral microbiomes in 66 healthy adults from the United Kingdom and Sweden in a two-center randomized placebo-controlled clinical trial. Feces and saliva were collected at baseline, immediately after exposure, and 1, 2, 4, and 12 months after administration of antibiotics or placebo. Sequences of 16S rRNA gene amplicons from all samples and metagenomic shotgun sequences from selected baseline and post-antibiotic-treatment sample pairs were analyzed. Additionally, metagenomic predictions based on 16S rRNA gene amplicon data were performed using PICRUSt. The salivary microbiome was found to be significantly more robust, whereas the antibiotics negatively affected the fecal microbiome: in particular, health-associated butyrate-producing species became strongly underrepresented. Additionally, exposure to different antibiotics enriched genes associated with antibiotic resistance. In conclusion, healthy individuals, exposed to a single antibiotic treatment, undergo considerable microbial shifts and enrichment in antibiotic resistance in their feces, while their salivary microbiome composition remains unexpectedly stable. The health-related consequences for the gut microbiome should increase the awareness of the individual risks involved with antibiotic use, especially in a (diseased) population with an already dysregulated microbiome. On the other hand, understanding the mechanisms behind the resilience of the oral microbiome toward ecological collapse might prove useful in combating microbial dysbiosis elsewhere in the body.

Importance: Many health care professionals use antibiotic prophylaxis strategies to prevent infection after surgery. This practice is under debate since it enhances the spread of antibiotic resistance. Another important reason to avoid nonessential use of antibiotics, the impact on our microbiome, has hardly received attention. In this study, we assessed the impact of antibiotics on the human microbial ecology at two niches. We followed the oral and gut microbiomes in 66 individuals from before, immediately after, and up to 12 months after exposure to different antibiotic classes. The salivary microbiome recovered quickly and was surprisingly robust toward antibiotic-induced disturbance. The fecal microbiome was severely affected by most antibiotics: for months, health-associated butyrate-producing species became strongly underrepresented. Additionally, there was an enrichment of genes associated with antibiotic resistance. Clearly, even a single antibiotic treatment in healthy individuals contributes to the risk of resistance development and leads to long-lasting detrimental shifts in the gut microbiome.

FIG 1

Comparison of baseline microbiome profiles from both types of samples, saliva and feces (A), and per sample type, feces (B) and saliva (C), by study site, KI (Sweden) and HP (United Kingdom). The PCA plot is based on randomly subsampled and log₂-transformed OTU data. The data set included 37 saliva-feces baseline sample pairs from the HP study and 29 from the KI study. The red ellipse highlights the two “types” of fecal samples—Prevotella- and Bacteroides-dominated samples, respectively.

FIG 2

Effects of antibiotics on microbiome profiles of feces (A) and saliva (B) from the KI study and feces (C) and saliva (D) from the HP study. The PCA plot is based on log₂-transformed OTU data. Different colors indicate different time points; different symbols indicate different treatment groups. Outliers in the KI (A) and HP (C) fecal data sets are highlighted with the respective subject number.

FIG 3

Similarity in microbiome profiles between the baseline (BL) and the other visits (W1, week 1; M1, month 1; M2, month 2; M4, month 4; M12, month 12). The horizontal bar indicates the mean value; the error bar indicates the 95% confidence interval. Bray-Curtis similarities were calculated between the log₂-transformed microbiome profiles of the baseline and each of the other time point samples of the respective individual. Brackets connect statistically significantly different groups within each visit pair (P < 0.05; one-way analysis of variance, Games-Howell post hoc test).

FIG 4

Relative abundance of the predicted KEGG orthologous groups (KOs) in the fecal (A) and salivary (B) samples from the clindamycin group plotted against the samples from the KI placebo group per individual time point. Error lines indicate standard deviations. No significant differences were observed in saliva, while in feces, 3 KOs at 1 week post-antibiotic treatment and 512 of the 4,606 predicted KOs at 1 month post-antibiotic treatment were significantly different in their proportions from the placebo group (P < 0.005, Welch’s t test, Welch’s inverted confidence interval method, and Storey FDR correction for multiple comparisons).

FIG 5

Most significantly affected KOs (26 of 520) in the predicted metagenomes from clindamycin-exposed feces at month 1 compared to the respective placebo group samples. FAD, flavin adenine dinucleotide; SEPHS, selenophosphate synthetase.